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Determining carbon-carbon double bond position of unsaturated glycerophospholipids in human plasma NIST® SRM® 1950 by electron impact excitation of ions from organics-tandem mass spectrometry (EIEIO-MS/MS)

2025/07/24 by Sara Martínez, Ana Gradillas, Hana Cermakova +2 · 1 voice
Biochemistry, Genetics and Molecular Biology · Chemistry · #Metabolomics and Mass Spectrometry Studies #Mass Spectrometry Techniques and Applications #Metabolism and Genetic Disorders

paper · doi:10.1016/j.jpba.2025.117081

openalex publication_date 2025/07/24 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Understanding the structural diversity and biological functions of unsaturated fatty acyl chains (FAC) esterified in complex lipids -such as glycerolipids (GL), glycerophospholipids (GP) or sphingolipids (SP)- requires precise knowledge of the degree of unsaturation, location, and geometrical isomerism of the carbon-carbon double bonds (CC). However, the complex isomeric nature of lipids, combined with the inherent limitations of conventional tandem mass spectrometry (MS/MS) in structural elucidation, remains a major challenge in accurate CC elucidation. To overcome this, advanced MS/MS strategies, such as electron impact excitation of ions from organics (EIEIO) have emerged, generating diagnostic fragment ions that enable unambiguous CC localization. In the present study, we conducted a qualitative structural analysis of the CC positions in esterified FAC of GP present in NIST® Human Plasma Standard Reference Material, SRM 1950, employing RP-UHPLC-ESI(+)-EIEIO-QTOF-MS/MS. Interpretation of ESI(+)-EIEIO-MS/MS spectra enabled CC determination in 120 unsaturated GP, revealing a predominance of ω-6 and ω-3 FAC. These results offer new insights into the FAC distribution of this reference material, enhancing the structural annotation confidence level. By integrating such detailed molecular information, EIEIO-MS/MS proves to be a powerful approach for in-depth lipid structural elucidation in complex biological matrices, thereby contributing to methodological advancements and supporting its future application in translational lipidomics.

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